AMD vs NVIDIA Inference Benchmark by SemiAnalysis
Here are the contents in English:
Summary
In today’s AI computing arms race, NVIDIA’s GPU has become a standard in global data centers, while AMD’s products seem to be outside of mainstream view. Recently, semiconductor analysis agency SemiAnalysis completed a long-term depth report after 6 months of research, covering comprehensive comparisons and explorations of reasoning performance, total ownership cost, rental market ecosystem, etc.
Summary
Test results show that MI300X still lacks sufficient competitiveness compared to H200 in most scenarios, but AMD has shown unique advantages in certain fields. For example, in testing of large models such as Llama3 405B and DeepSeekV3 670B, MI300X’s absolute performance and cost-effectiveness surpassed NVIDIA’s H100.
Summary
Overall, for super-large-scale enterprises, direct procurement of AMD GPU and long-term operation may be more economically viable, but for medium-sized or small businesses with temporary computing needs, the high costs and service scarcity of rental markets make AMD almost an impossible option.
Key Points
- NVIDIA’s GPU has become a standard in global data centers, while AMD’s products seem to be outside of mainstream view.
- SemiAnalysis completed a long-term depth report after 6 months of research on comprehensive comparisons of reasoning performance of the two companies.
- Test results show that MI300X still lacks sufficient competitiveness compared to H200 in most scenarios, but AMD has shown unique advantages in certain fields.
- For super-large-scale enterprises, direct procurement of AMD GPU and long-term operation may be more economically viable, but for medium-sized or small businesses with temporary computing needs, the high costs and service scarcity of rental markets make AMD almost an impossible option.
- SemiAnalysis analysis suggests that AMD needs to improve its competitiveness in the rental market and software ecosystem construction quality to compete with NVIDIA.
Translation
Summary
在人工智能算力军备竞赛的今天，NVIDIA的GPU几乎成为了全球数据中心的标配，而AMD的产品却似乎总是游离在主流视野之外。最近，半导体分析机构SemiAnalysis耗时6个月完成了一份超长的深度报告，涵盖了两家公司推理性能的全方位对比，并探讨了总拥有成本、租赁市场生态等关键议题。
Summary
测试结果显示，在大多数场景中，MI300X与H200相比仍然缺乏足够的竞争力，但是在某些特定领域，AMD还是展现出了其独特的优势。例如，在Llama3 405B和DeepSeekV3 670B等大型模型的测试中，MI300X的绝对性能和性价比反超了英伟达的H100。
Summary
总体来说，对于超大规模企业而言直接采购AMD GPU并且进行长期运营可能是更具有经济性的，但对于中小型企业或者有临时算力需求的企业来说租赁市场的高成本和服务稀缺性使得AMD几乎成为一个不可能的选项。
Key Points
- NVIDIA的GPU已经成为全球数据中心的标配，而AMD的产品似乎总是游离在主流视野之外。
- SemiAnalysis耗时6个月完成了一份超长的深度报告，涵盖了两家公司推理性能的全方位对比。
- 测试结果显示，在大多数场景中，MI300X与H200相比仍然缺乏足够的竞争力，但是在某些特定领域，AMD还是展现出了其独特的优势。
- 对于超大规模企业而言直接采购AMD GPU并且进行长期运营可能是更具有经济性的，但对于中小型企业或者有临时算力需求的企业来说租赁市场的高成本和服务稀缺性使得AMD几乎成为一个不可能的选项。
- SemiAnalysis分析，AMD需要提高其租赁市场的竞争力和软件生态建设的质量，以便能够与NVIDIA竞争。
Reference:
https://semianalysis.com/2025/05/23/amd-vs-nvidia-inference-benchmark-who-wins-performance-cost-per-million-tokens/